{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集形状: (60000, 28, 28)\n",
      "训练集标签数量: 60000\n",
      "测试集形状: (10000, 28, 28)\n",
      "测试集标签数量: 10000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fashion MNIST数据集包含10个类别的服装图片:\n",
      "0: T-shirt/top\n",
      "1: 裤子\n",
      "2: 套头衫\n",
      "3: 连衣裙\n",
      "4: 外套\n",
      "5: 凉鞋\n",
      "6: 衬衫\n",
      "7: 运动鞋\n",
      "8: 包\n",
      "9: 短靴\n",
      "\n",
      "每张图片是28x28像素的灰度图\n",
      "训练集包含60,000张图片，测试集包含10,000张图片\n",
      "像素值范围为0-255\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "\n",
    "# 加载Fashion MNIST数据集\n",
    "train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True,transform=torchvision.transforms.ToTensor())\n",
    "test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True,transform=torchvision.transforms.ToTensor())\n",
    "\n",
    "train_images = train_dataset.data.numpy()\n",
    "train_labels = train_dataset.targets.numpy()\n",
    "test_images = test_dataset.data.numpy() \n",
    "test_labels = test_dataset.targets.numpy()\n",
    "\n",
    "# 定义类别名称\n",
    "class_names = ['T-shirt/top', '裤子', '套头衫', '连衣裙', '外套',\n",
    "               '凉鞋', '衬衫', '运动鞋', '包', '短靴']\n",
    "\n",
    "# 打印数据集基本信息\n",
    "print(\"训练集形状:\", train_images.shape)\n",
    "print(\"训练集标签数量:\", len(train_labels))\n",
    "print(\"测试集形状:\", test_images.shape)\n",
    "print(\"测试集标签数量:\", len(test_labels))\n",
    "\n",
    "# 显示第一张图片\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "# 显示前25张训练图片\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n",
    "\n",
    "# 数据集说明\n",
    "print(\"\\nFashion MNIST数据集包含10个类别的服装图片:\")\n",
    "for i, name in enumerate(class_names):\n",
    "    print(f\"{i}: {name}\")\n",
    "print(\"\\n每张图片是28x28像素的灰度图\")\n",
    "print(\"训练集包含60,000张图片，测试集包含10,000张图片\") \n",
    "print(\"像素值范围为0-255\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
      "    0   1   4   0   0   0   0   1   1   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
      "   54   0   0   0   1   3   4   0   0   3]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
      "  144 123  23   0   0   0   0  12  10   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
      "  107 156 161 109  64  23  77 130  72  15]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
      "  216 163 127 121 122 146 141  88 172  66]\n",
      " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
      "  223 223 215 213 164 127 123 196 229   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
      "  235 227 224 222 224 221 223 245 173   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
      "  180 212 210 211 213 223 220 243 202   0]\n",
      " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
      "  169 227 208 218 224 212 226 197 209  52]\n",
      " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
      "  198 221 215 213 222 220 245 119 167  56]\n",
      " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
      "  232 213 218 223 234 217 217 209  92   0]\n",
      " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
      "  222 221 216 223 229 215 218 255  77   0]\n",
      " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
      "  211 218 224 223 219 215 224 244 159   0]\n",
      " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
      "  224 234 176 188 250 248 233 238 215   0]\n",
      " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
      "  255 255 221 234 221 211 220 232 246   0]\n",
      " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
      "  188 154 191 210 204 209 222 228 225   0]\n",
      " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
      "  168 219 221 215 217 223 223 224 229  29]\n",
      " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
      "  239 223 218 212 209 222 220 221 230  67]\n",
      " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
      "  199 206 186 181 177 172 181 205 206 115]\n",
      " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
      "  195 191 198 192 176 156 167 177 210  92]\n",
      " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
      "  210 210 211 188 188 194 192 216 170   0]\n",
      " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
      "  182 182 181 176 166 168  99  58   0   0]\n",
      " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_dataset[i][0].squeeze(), cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_dataset[i][1]])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 把数据集划分训练集55000和验证集5000，并给DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 55000\n",
      "验证集大小: 5000\n",
      "测试集大小: 10000\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集和验证集\n",
    "train_size = 55000\n",
    "val_size = 5000\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size],generator=torch.Generator().manual_seed(42))\n",
    "\n",
    "# 创建DataLoader\n",
    "batch_size = 64\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "#shuffle=true表示打乱数据集\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)\n",
    "\n",
    "print(f'训练集大小: {len(train_dataset)}')\n",
    "print(f'验证集大小: {len(val_dataset)}')\n",
    "print(f'测试集大小: {len(test_dataset)}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每个批次的大小: 64\n",
      "训练批次数: 860\n"
     ]
    }
   ],
   "source": [
    "# 打印批次大小和训练批次数\n",
    "print(f'每个批次的大小: {batch_size}')\n",
    "print(f'训练批次数: {len(train_loader)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 搭建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuralNetwork(\n",
      "  (fc1): Linear(in_features=784, out_features=300, bias=True)\n",
      "  (fc2): Linear(in_features=300, out_features=100, bias=True)\n",
      "  (fc3): Linear(in_features=100, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "# 定义神经网络模型\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        # 输入层到第一个隐藏层 (28*28 -> 300)\n",
    "        self.fc1 = nn.Linear(28*28, 300)\n",
    "        # 第一个隐藏层到第二个隐藏层 (300 -> 100)\n",
    "        self.fc2 = nn.Linear(300, 100)\n",
    "        # 第二个隐藏层到输出层 (100 -> 10)\n",
    "        self.fc3 = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        #print('输入形状:', x.shape)\n",
    "        # 将输入展平为一维向量\n",
    "        x = x.view(-1, 28*28)\n",
    "        #print('展平后形状:', x.shape)\n",
    "        # 第一个隐藏层使用ReLU激活函数\n",
    "        x = F.relu(self.fc1(x))\n",
    "        #print('第一个隐藏层输出形状:', x.shape)\n",
    "        # 第二个隐藏层使用ReLU激活函数\n",
    "        x = F.relu(self.fc2(x))\n",
    "        #print('第二个隐藏层输出形状:', x.shape)\n",
    "        # 输出层不使用激活函数\n",
    "        x = self.fc3(x)\n",
    "        #print('输出层形状:', x.shape)\n",
    "        return x\n",
    "\n",
    "# 创建模型实例\n",
    "model = NeuralNetwork()\n",
    "print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入数据形状: torch.Size([64, 1, 28, 28])\n",
      "标签形状: torch.Size([64])\n",
      "\n",
      "模型输出形状: torch.Size([64, 10])\n",
      "预测结果: tensor([0, 0, 4, 4, 0, 0, 4, 0, 0, 4, 4, 0, 7, 0, 0, 4, 4, 6, 4, 0, 0, 0, 0, 4,\n",
      "        0, 4, 9, 4, 0, 4, 4, 4, 7, 0, 0, 0, 4, 4, 0, 7, 4, 0, 4, 7, 4, 4, 0, 4,\n",
      "        0, 4, 4, 0, 9, 0, 6, 0, 0, 4, 9, 0, 4, 0, 4, 4])\n",
      "真实标签: tensor([4, 3, 2, 5, 0, 0, 4, 2, 2, 4, 4, 0, 8, 8, 5, 2, 4, 9, 4, 7, 6, 1, 4, 2,\n",
      "        0, 5, 1, 3, 9, 9, 5, 3, 8, 1, 6, 4, 4, 5, 0, 6, 8, 1, 3, 6, 9, 3, 5, 7,\n",
      "        9, 1, 3, 2, 6, 6, 9, 0, 2, 5, 8, 4, 7, 3, 2, 3])\n"
     ]
    }
   ],
   "source": [
    "# 获取第一个批次的数据\n",
    "images, labels = next(iter(train_loader))\n",
    "\n",
    "# 打印输入数据的形状\n",
    "print('输入数据形状:', images.shape)\n",
    "print('标签形状:', labels.shape)\n",
    "\n",
    "# 前向传播\n",
    "outputs = model(images)\n",
    "\n",
    "# 打印输出结果\n",
    "print('\\n模型输出形状:', outputs.shape)\n",
    "print('预测结果:', torch.argmax(outputs, dim=1))\n",
    "print('真实标签:', labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型总参数量: 266610\n",
      "fc1.weight 的参数量: 235200\n",
      "fc1.bias 的参数量: 300\n",
      "fc2.weight 的参数量: 30000\n",
      "fc2.bias 的参数量: 100\n",
      "fc3.weight 的参数量: 1000\n",
      "fc3.bias 的参数量: 10\n"
     ]
    }
   ],
   "source": [
    "# 计算模型总参数量\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print('模型总参数量:', total_params)\n",
    "\n",
    "# 打印每一层的参数量\n",
    "for name, param in model.named_parameters():\n",
    "    print(f'{name} 的参数量: {param.numel()}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 设置交叉熵损失函数，SGD优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "损失函数: CrossEntropyLoss()\n",
      "优化器: SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    differentiable: False\n",
      "    foreach: None\n",
      "    fused: None\n",
      "    lr: 0.01\n",
      "    maximize: False\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import torch.optim as optim\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()#交叉熵损失函数\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)#lr是学习率，momentum是动量\n",
    "\n",
    "print('损失函数:', criterion)\n",
    "print('优化器:', optimizer)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.3\n",
      "numpy 2.1.3\n",
      "pandas 2.3.0\n",
      "sklearn 1.7.0\n",
      "torch 2.7.1+cu126\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import torch\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编写评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, test_loader, compute_loss=False):\n",
    "    \"\"\"\n",
    "    评估模型在测试集上的准确率和损失\n",
    "    \n",
    "    参数:\n",
    "        model: 神经网络模型\n",
    "        test_loader: 测试数据加载器\n",
    "        compute_loss: 是否计算损失,默认为False\n",
    "    \n",
    "    返回:\n",
    "        accuracy: 准确率\n",
    "        test_loss: 如果compute_loss为True则返回测试损失,否则返回None\n",
    "    \"\"\"\n",
    "    model.eval()  # 设置为评估模式\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    test_loss = 0\n",
    "    \n",
    "    if compute_loss:\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "    with torch.no_grad():  # 不计算梯度\n",
    "        for images, labels in test_loader:\n",
    "            images,labels=images.to(device),labels.to(device)# 将数据移动到GPU\n",
    "            outputs = model(images)\n",
    "            if compute_loss:\n",
    "                loss = criterion(outputs, labels)\n",
    "                test_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1) # 返回两个值，第一个是最大值，第二个是最大值的索引\n",
    "            total += labels.size(0) # 标签的个数\n",
    "            correct += (predicted == labels).sum().item()# 预测正确的个数\n",
    "    \n",
    "    accuracy = 100 * correct / total\n",
    "    #print(f'准确率: {accuracy:.2f}%')\n",
    "    \n",
    "    if compute_loss:\n",
    "        test_loss = test_loss / len(test_loader)\n",
    "        #print(f'损失: {test_loss:.4f}')\n",
    "        return accuracy, test_loss\n",
    "    return accuracy, None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编写训练函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs):\n",
    "    \"\"\"\n",
    "    训练神经网络模型\n",
    "    \n",
    "    参数:\n",
    "        model: 神经网络模型\n",
    "        train_loader: 训练数据加载器\n",
    "        val_loader: 验证数据加载器\n",
    "        criterion: 损失函数\n",
    "        optimizer: 优化器\n",
    "        num_epochs: 训练轮数\n",
    "    \n",
    "    返回:\n",
    "        model: 训练好的模型\n",
    "        history: 训练历史记录,包含每个epoch的训练和验证指标\n",
    "    \"\"\"\n",
    "    # 将模型移动到GPU\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model = model.to(device)\n",
    "    \n",
    "    model.train()  # 设置为训练模式\n",
    "    history = {\n",
    "        'train_loss': [],\n",
    "        'train_acc': [],\n",
    "        'val_loss': [],\n",
    "        'val_acc': []\n",
    "    }\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        running_loss = 0.0\n",
    "        for i, (images, labels) in enumerate(train_loader):\n",
    "            images, labels = images.to(device), labels.to(device)  # 将数据移动到GPU\n",
    "            \n",
    "            optimizer.zero_grad()  # 清除之前的梯度\n",
    "            # 前向传播\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # 反向传播和优化\n",
    "            loss.backward()  # 反向传播\n",
    "            optimizer.step()  # 更新参数\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "            \n",
    "            # 每100个批次打印一次训练状态\n",
    "            if (i + 1) % 100 == 0:\n",
    "                # print(f'轮次 [{epoch+1}/{num_epochs}], 批次 [{i+1}/{len(train_loader)}], '\n",
    "                #       f'损失: {running_loss/100:.4f}')\n",
    "                running_loss = 0.0\n",
    "                \n",
    "        \n",
    "        # 在每个epoch结束后评估模型\n",
    "        print(f'\\nEpoch {epoch+1} 完成')\n",
    "        \n",
    "        # 计算训练集准确率和损失\n",
    "        \n",
    "        train_acc, train_loss = evaluate(model, train_loader, compute_loss=True)\n",
    "        print(f'训练集准确率:{train_acc:.2f}%,训练集损失：{train_loss:.4f}')\n",
    "        history['train_acc'].append(train_acc)\n",
    "        history['train_loss'].append(train_loss)\n",
    "        \n",
    "        # 计算验证集准确率和损失\n",
    "        \n",
    "        val_acc, val_loss = evaluate(model, val_loader, compute_loss=True)\n",
    "        print(f'验证集准确率：{val_acc:.2f}%,验证集损失：{val_loss:.4f}')\n",
    "        history['val_acc'].append(val_acc)\n",
    "        history['val_loss'].append(val_loss)\n",
    "        \n",
    "        model.train()  # 切回训练模式\n",
    "        \n",
    "    print('训练完成!')\n",
    "    return model, history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda:0 device\n",
      "轮次 [1/20], 批次 [100/860], 损失: 1.6493\n",
      "轮次 [1/20], 批次 [200/860], 损失: 0.8058\n",
      "轮次 [1/20], 批次 [300/860], 损失: 0.6748\n",
      "轮次 [1/20], 批次 [400/860], 损失: 0.5910\n",
      "轮次 [1/20], 批次 [500/860], 损失: 0.5633\n",
      "轮次 [1/20], 批次 [600/860], 损失: 0.5354\n",
      "轮次 [1/20], 批次 [700/860], 损失: 0.4927\n",
      "轮次 [1/20], 批次 [800/860], 损失: 0.5005\n",
      "\n",
      "Epoch 1 完成\n",
      "训练集准确率:83.31%,训练集损失：0.4823\n",
      "验证集准确率：82.04%,验证集损失：0.5243\n",
      "轮次 [2/20], 批次 [100/860], 损失: 0.4396\n",
      "轮次 [2/20], 批次 [200/860], 损失: 0.4719\n",
      "轮次 [2/20], 批次 [300/860], 损失: 0.4522\n",
      "轮次 [2/20], 批次 [400/860], 损失: 0.4473\n",
      "轮次 [2/20], 批次 [500/860], 损失: 0.4152\n",
      "轮次 [2/20], 批次 [600/860], 损失: 0.4341\n",
      "轮次 [2/20], 批次 [700/860], 损失: 0.4221\n",
      "轮次 [2/20], 批次 [800/860], 损失: 0.4120\n",
      "\n",
      "Epoch 2 完成\n",
      "训练集准确率:85.39%,训练集损失：0.3994\n",
      "验证集准确率：84.30%,验证集损失：0.4306\n",
      "轮次 [3/20], 批次 [100/860], 损失: 0.3910\n",
      "轮次 [3/20], 批次 [200/860], 损失: 0.3990\n",
      "轮次 [3/20], 批次 [300/860], 损失: 0.3768\n",
      "轮次 [3/20], 批次 [400/860], 损失: 0.3932\n",
      "轮次 [3/20], 批次 [500/860], 损失: 0.3940\n",
      "轮次 [3/20], 批次 [600/860], 损失: 0.3696\n",
      "轮次 [3/20], 批次 [700/860], 损失: 0.3839\n",
      "轮次 [3/20], 批次 [800/860], 损失: 0.3772\n",
      "\n",
      "Epoch 3 完成\n",
      "训练集准确率:86.87%,训练集损失：0.3641\n",
      "验证集准确率：86.06%,验证集损失：0.3875\n",
      "轮次 [4/20], 批次 [100/860], 损失: 0.3613\n",
      "轮次 [4/20], 批次 [200/860], 损失: 0.3662\n",
      "轮次 [4/20], 批次 [300/860], 损失: 0.3589\n",
      "轮次 [4/20], 批次 [400/860], 损失: 0.3449\n",
      "轮次 [4/20], 批次 [500/860], 损失: 0.3518\n",
      "轮次 [4/20], 批次 [600/860], 损失: 0.3604\n",
      "轮次 [4/20], 批次 [700/860], 损失: 0.3504\n",
      "轮次 [4/20], 批次 [800/860], 损失: 0.3448\n",
      "\n",
      "Epoch 4 完成\n",
      "训练集准确率:86.71%,训练集损失：0.3640\n",
      "验证集准确率：85.10%,验证集损失：0.3940\n",
      "轮次 [5/20], 批次 [100/860], 损失: 0.3381\n",
      "轮次 [5/20], 批次 [200/860], 损失: 0.3500\n",
      "轮次 [5/20], 批次 [300/860], 损失: 0.3335\n",
      "轮次 [5/20], 批次 [400/860], 损失: 0.3344\n",
      "轮次 [5/20], 批次 [500/860], 损失: 0.3411\n",
      "轮次 [5/20], 批次 [600/860], 损失: 0.3199\n",
      "轮次 [5/20], 批次 [700/860], 损失: 0.3463\n",
      "轮次 [5/20], 批次 [800/860], 损失: 0.3421\n",
      "\n",
      "Epoch 5 完成\n",
      "训练集准确率:88.29%,训练集损失：0.3152\n",
      "验证集准确率：86.32%,验证集损失：0.3619\n",
      "轮次 [6/20], 批次 [100/860], 损失: 0.3316\n",
      "轮次 [6/20], 批次 [200/860], 损失: 0.3240\n",
      "轮次 [6/20], 批次 [300/860], 损失: 0.3312\n",
      "轮次 [6/20], 批次 [400/860], 损失: 0.3063\n",
      "轮次 [6/20], 批次 [500/860], 损失: 0.3045\n",
      "轮次 [6/20], 批次 [600/860], 损失: 0.3038\n",
      "轮次 [6/20], 批次 [700/860], 损失: 0.3197\n",
      "轮次 [6/20], 批次 [800/860], 损失: 0.3119\n",
      "\n",
      "Epoch 6 完成\n",
      "训练集准确率:88.34%,训练集损失：0.3146\n",
      "验证集准确率：87.06%,验证集损失：0.3564\n",
      "轮次 [7/20], 批次 [100/860], 损失: 0.2988\n",
      "轮次 [7/20], 批次 [200/860], 损失: 0.3098\n",
      "轮次 [7/20], 批次 [300/860], 损失: 0.3046\n",
      "轮次 [7/20], 批次 [400/860], 损失: 0.3003\n",
      "轮次 [7/20], 批次 [500/860], 损失: 0.3075\n",
      "轮次 [7/20], 批次 [600/860], 损失: 0.3069\n",
      "轮次 [7/20], 批次 [700/860], 损失: 0.2909\n",
      "轮次 [7/20], 批次 [800/860], 损失: 0.2999\n",
      "\n",
      "Epoch 7 完成\n",
      "训练集准确率:89.43%,训练集损失：0.2891\n",
      "验证集准确率：87.28%,验证集损失：0.3379\n",
      "轮次 [8/20], 批次 [100/860], 损失: 0.2826\n",
      "轮次 [8/20], 批次 [200/860], 损失: 0.2910\n",
      "轮次 [8/20], 批次 [300/860], 损失: 0.2856\n",
      "轮次 [8/20], 批次 [400/860], 损失: 0.2885\n",
      "轮次 [8/20], 批次 [500/860], 损失: 0.2833\n",
      "轮次 [8/20], 批次 [600/860], 损失: 0.2938\n",
      "轮次 [8/20], 批次 [700/860], 损失: 0.2835\n",
      "轮次 [8/20], 批次 [800/860], 损失: 0.2905\n",
      "\n",
      "Epoch 8 完成\n",
      "训练集准确率:90.15%,训练集损失：0.2708\n",
      "验证集准确率：88.06%,验证集损失：0.3272\n",
      "轮次 [9/20], 批次 [100/860], 损失: 0.2829\n",
      "轮次 [9/20], 批次 [200/860], 损失: 0.2841\n",
      "轮次 [9/20], 批次 [300/860], 损失: 0.2806\n",
      "轮次 [9/20], 批次 [400/860], 损失: 0.2777\n",
      "轮次 [9/20], 批次 [500/860], 损失: 0.2857\n",
      "轮次 [9/20], 批次 [600/860], 损失: 0.2791\n",
      "轮次 [9/20], 批次 [700/860], 损失: 0.2806\n",
      "轮次 [9/20], 批次 [800/860], 损失: 0.2859\n",
      "\n",
      "Epoch 9 完成\n",
      "训练集准确率:89.54%,训练集损失：0.2792\n",
      "验证集准确率：86.70%,验证集损失：0.3384\n",
      "轮次 [10/20], 批次 [100/860], 损失: 0.2722\n",
      "轮次 [10/20], 批次 [200/860], 损失: 0.2638\n",
      "轮次 [10/20], 批次 [300/860], 损失: 0.2767\n",
      "轮次 [10/20], 批次 [400/860], 损失: 0.2612\n",
      "轮次 [10/20], 批次 [500/860], 损失: 0.2700\n",
      "轮次 [10/20], 批次 [600/860], 损失: 0.2700\n",
      "轮次 [10/20], 批次 [700/860], 损失: 0.2730\n",
      "轮次 [10/20], 批次 [800/860], 损失: 0.2612\n",
      "\n",
      "Epoch 10 完成\n",
      "训练集准确率:90.53%,训练集损失：0.2542\n",
      "验证集准确率：87.78%,验证集损失：0.3235\n",
      "轮次 [11/20], 批次 [100/860], 损失: 0.2668\n",
      "轮次 [11/20], 批次 [200/860], 损失: 0.2546\n",
      "轮次 [11/20], 批次 [300/860], 损失: 0.2602\n",
      "轮次 [11/20], 批次 [400/860], 损失: 0.2779\n",
      "轮次 [11/20], 批次 [500/860], 损失: 0.2484\n",
      "轮次 [11/20], 批次 [600/860], 损失: 0.2645\n",
      "轮次 [11/20], 批次 [700/860], 损失: 0.2562\n",
      "轮次 [11/20], 批次 [800/860], 损失: 0.2647\n",
      "\n",
      "Epoch 11 完成\n",
      "训练集准确率:91.37%,训练集损失：0.2387\n",
      "验证集准确率：88.48%,验证集损失：0.3117\n",
      "轮次 [12/20], 批次 [100/860], 损失: 0.2466\n",
      "轮次 [12/20], 批次 [200/860], 损失: 0.2525\n",
      "轮次 [12/20], 批次 [300/860], 损失: 0.2369\n",
      "轮次 [12/20], 批次 [400/860], 损失: 0.2638\n",
      "轮次 [12/20], 批次 [500/860], 损失: 0.2620\n",
      "轮次 [12/20], 批次 [600/860], 损失: 0.2579\n",
      "轮次 [12/20], 批次 [700/860], 损失: 0.2604\n",
      "轮次 [12/20], 批次 [800/860], 损失: 0.2546\n",
      "\n",
      "Epoch 12 完成\n",
      "训练集准确率:91.24%,训练集损失：0.2385\n",
      "验证集准确率：88.32%,验证集损失：0.3145\n",
      "轮次 [13/20], 批次 [100/860], 损失: 0.2402\n",
      "轮次 [13/20], 批次 [200/860], 损失: 0.2545\n",
      "轮次 [13/20], 批次 [300/860], 损失: 0.2375\n",
      "轮次 [13/20], 批次 [400/860], 损失: 0.2562\n",
      "轮次 [13/20], 批次 [500/860], 损失: 0.2425\n",
      "轮次 [13/20], 批次 [600/860], 损失: 0.2413\n",
      "轮次 [13/20], 批次 [700/860], 损失: 0.2478\n",
      "轮次 [13/20], 批次 [800/860], 损失: 0.2333\n",
      "\n",
      "Epoch 13 完成\n",
      "训练集准确率:91.26%,训练集损失：0.2360\n",
      "验证集准确率：88.24%,验证集损失：0.3255\n",
      "轮次 [14/20], 批次 [100/860], 损失: 0.2275\n",
      "轮次 [14/20], 批次 [200/860], 损失: 0.2290\n",
      "轮次 [14/20], 批次 [300/860], 损失: 0.2328\n",
      "轮次 [14/20], 批次 [400/860], 损失: 0.2467\n",
      "轮次 [14/20], 批次 [500/860], 损失: 0.2466\n",
      "轮次 [14/20], 批次 [600/860], 损失: 0.2486\n",
      "轮次 [14/20], 批次 [700/860], 损失: 0.2413\n",
      "轮次 [14/20], 批次 [800/860], 损失: 0.2290\n",
      "\n",
      "Epoch 14 完成\n",
      "训练集准确率:91.53%,训练集损失：0.2284\n",
      "验证集准确率：88.24%,验证集损失：0.3211\n",
      "轮次 [15/20], 批次 [100/860], 损失: 0.2316\n",
      "轮次 [15/20], 批次 [200/860], 损失: 0.2284\n",
      "轮次 [15/20], 批次 [300/860], 损失: 0.2447\n",
      "轮次 [15/20], 批次 [400/860], 损失: 0.2263\n",
      "轮次 [15/20], 批次 [500/860], 损失: 0.2375\n",
      "轮次 [15/20], 批次 [600/860], 损失: 0.2313\n",
      "轮次 [15/20], 批次 [700/860], 损失: 0.2201\n",
      "轮次 [15/20], 批次 [800/860], 损失: 0.2496\n",
      "\n",
      "Epoch 15 完成\n",
      "训练集准确率:91.37%,训练集损失：0.2322\n",
      "验证集准确率：87.62%,验证集损失：0.3328\n",
      "轮次 [16/20], 批次 [100/860], 损失: 0.2191\n",
      "轮次 [16/20], 批次 [200/860], 损失: 0.2282\n",
      "轮次 [16/20], 批次 [300/860], 损失: 0.2246\n",
      "轮次 [16/20], 批次 [400/860], 损失: 0.2169\n",
      "轮次 [16/20], 批次 [500/860], 损失: 0.2267\n",
      "轮次 [16/20], 批次 [600/860], 损失: 0.2266\n",
      "轮次 [16/20], 批次 [700/860], 损失: 0.2253\n",
      "轮次 [16/20], 批次 [800/860], 损失: 0.2265\n",
      "\n",
      "Epoch 16 完成\n",
      "训练集准确率:91.73%,训练集损失：0.2216\n",
      "验证集准确率：87.96%,验证集损失：0.3229\n",
      "轮次 [17/20], 批次 [100/860], 损失: 0.2120\n",
      "轮次 [17/20], 批次 [200/860], 损失: 0.2194\n",
      "轮次 [17/20], 批次 [300/860], 损失: 0.2218\n",
      "轮次 [17/20], 批次 [400/860], 损失: 0.2159\n",
      "轮次 [17/20], 批次 [500/860], 损失: 0.2200\n",
      "轮次 [17/20], 批次 [600/860], 损失: 0.2230\n",
      "轮次 [17/20], 批次 [700/860], 损失: 0.2217\n",
      "轮次 [17/20], 批次 [800/860], 损失: 0.2176\n",
      "\n",
      "Epoch 17 完成\n",
      "训练集准确率:91.83%,训练集损失：0.2157\n",
      "验证集准确率：87.78%,验证集损失：0.3308\n",
      "轮次 [18/20], 批次 [100/860], 损失: 0.2076\n",
      "轮次 [18/20], 批次 [200/860], 损失: 0.2080\n",
      "轮次 [18/20], 批次 [300/860], 损失: 0.2020\n",
      "轮次 [18/20], 批次 [400/860], 损失: 0.2175\n",
      "轮次 [18/20], 批次 [500/860], 损失: 0.2109\n",
      "轮次 [18/20], 批次 [600/860], 损失: 0.2110\n",
      "轮次 [18/20], 批次 [700/860], 损失: 0.2117\n",
      "轮次 [18/20], 批次 [800/860], 损失: 0.2183\n",
      "\n",
      "Epoch 18 完成\n",
      "训练集准确率:92.71%,训练集损失：0.1964\n",
      "验证集准确率：88.74%,验证集损失：0.3016\n",
      "轮次 [19/20], 批次 [100/860], 损失: 0.2046\n",
      "轮次 [19/20], 批次 [200/860], 损失: 0.2093\n",
      "轮次 [19/20], 批次 [300/860], 损失: 0.1990\n",
      "轮次 [19/20], 批次 [400/860], 损失: 0.2162\n",
      "轮次 [19/20], 批次 [500/860], 损失: 0.2042\n",
      "轮次 [19/20], 批次 [600/860], 损失: 0.2118\n",
      "轮次 [19/20], 批次 [700/860], 损失: 0.2108\n",
      "轮次 [19/20], 批次 [800/860], 损失: 0.2008\n",
      "\n",
      "Epoch 19 完成\n",
      "训练集准确率:92.48%,训练集损失：0.2022\n",
      "验证集准确率：88.22%,验证集损失：0.3214\n",
      "轮次 [20/20], 批次 [100/860], 损失: 0.2089\n",
      "轮次 [20/20], 批次 [200/860], 损失: 0.1985\n",
      "轮次 [20/20], 批次 [300/860], 损失: 0.1888\n",
      "轮次 [20/20], 批次 [400/860], 损失: 0.2130\n",
      "轮次 [20/20], 批次 [500/860], 损失: 0.2111\n",
      "轮次 [20/20], 批次 [600/860], 损失: 0.2177\n",
      "轮次 [20/20], 批次 [700/860], 损失: 0.2159\n",
      "轮次 [20/20], 批次 [800/860], 损失: 0.1818\n",
      "\n",
      "Epoch 20 完成\n",
      "训练集准确率:93.05%,训练集损失：0.1864\n",
      "验证集准确率：88.66%,验证集损失：0.3176\n",
      "训练完成!\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(f'Using {device} device')\n",
    "model,history=train_model(model,train_loader,val_loader,criterion,optimizer,num_epochs=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制准确率和损失曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制训练和验证准确率曲线\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "epochs = range(1, len(history['train_acc']) + 1)\n",
    "plt.plot(epochs, history['train_acc'], label='训练准确率')\n",
    "plt.plot(epochs, history['val_acc'], label='验证准确率')\n",
    "plt.title('模型准确率')\n",
    "plt.xlabel('轮次')\n",
    "plt.ylabel('准确率')\n",
    "plt.legend()\n",
    "plt.xticks(epochs) # 设置横坐标为整数\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs, history['train_loss'], label='训练损失')\n",
    "plt.plot(epochs, history['val_loss'], label='验证损失')\n",
    "plt.title('模型损失')\n",
    "plt.xlabel('轮次') \n",
    "plt.ylabel('损失')\n",
    "plt.legend()\n",
    "plt.xticks(epochs) # 设置横坐标为整数\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 88.99%,测试集损失: 0.3309\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上评估模型\n",
    "model.eval()  # 设置为评估模式\n",
    "test_acc,test_loss=evaluate(model,test_loader,compute_loss=True)\n",
    "print(f'测试集准确率: {test_acc:.2f}%,测试集损失: {test_loss:.4f}')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
